Accurate Attitude Estimation for Drones in 5G Drone Small Cells

Abstract

In this paper, a new attitude estimation procedure for drones in 5G drone small cells (DSC) is described using array of antennas placed on the body. The proposed method utilizes a fractal structure for the locations of the receiver antennas. By applying the least square (LS) procedure on the received signals, the proposed method estimates the drone attitude more accurately than the state of the art attitude estimation method that exploits hexagonal antenna placement patterns. In order to improve the accuracy of the attitude estimation further, those initial estimated angles can be refined in a second phase by implementing the multiple signal classification (MUSIC) algorithm in two dimensions. The two-phase method is called fractal structure array (FSA). The simulation results indicate that by the employment of the second phase, the accuracy of the attitude estimation is enhanced considerably. In addition, larger fractal structure using more receiver antennas can be applied to improve the performance of the proposed estimation method even further. The computational complexity of the methods are also compared and it is concluded that even by the addition of the second phase, the computational complexity of the FSA method is lower than the other ones.